Introduction to Business Intelligence

Business intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current, and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, bench-marking, text mining, predictive analytics, and prescriptive analytics. BI technologies can handle large amounts of structured and sometimes unstructured data to help identify, develop, and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.

Business intelligence can be used by enterprises to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions involve priorities, goals, and directions at the broadest level. In all cases, BI is most effective when it combines data derived from the market in which a company operates (external data) with data from company sources internal to the business such as financial and operations data (internal data). When combined, external and internal data can provide a complete picture which, in effect, creates an “intelligence” that cannot be derived from any singular set of data. Among myriad uses, business intelligence tools empower organizations to gain insight into new markets, to assess demand and suitability of products and services for different market segments, and to gauge the impact of marketing efforts.

BI applications use data gathered from a data warehouse (DW) or from a data mart, and the concepts of BI and DW combine as “BI/DW” or as “BIDW”. A data warehouse contains a copy of analytical data that facilitate decision support.

According to Solomon Negash and Paul Gray, business intelligence (BI) can be defined as systems that combine:

  • Data gathering
  • Data storage
  • Knowledge management

with analysis to evaluate complex corporate and competitive information for presentation to planners and decision makers, with the objective of improving the timeliness and the quality of the input to the decision process.”

According to Forrester Research, business intelligence is “a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.” Under this definition, business intelligence encompasses information management (data integration, data quality, data warehousing, master-data management, text- and content-analytics, et al.). Therefore, Forrester refers to data preparation and data usage as two separate but closely linked segments of the business-intelligence architectural stack.

Some elements of business intelligence are:

  • Multidimensional aggregation and allocation
  • Denormalization, tagging, and standardization
  • Realtime reporting with analytical alert
  • A method of interfacing with unstructured data sources
  • Group consolidation, budgeting, and rolling forecasts
  • Statistical inference and probabilistic simulation
  • Key performance indicators optimization
  • Version control and process management
  • Open item management

Forrester distinguishes this from the business-intelligence market, which is “just the top layers of the BI architectural stack, such as reporting, analytics, and dashboards.”

Compared with competitive intelligence:

Though the term business intelligence is sometimes a synonym for competitive intelligence (because they both support decision making), BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes, and disseminates information with a topical focus on company competitors. If understood broadly, business intelligence can include the subset of competitive intelligence.

Compared with business analytics:

Business intelligence and business analytics are sometimes used interchangeably, but there are alternate definitions. Thomas Davenport, professor of information technology and management at Babson College argues that business intelligence should be divided into querying, reporting, Online analytical processing (OLAP), an “alerts” tool, and business analytics. In this definition, business analytics is the subset of BI focusing on statistics, prediction, and optimization, rather than the reporting functionality.

Business intelligence can be applied to the following business purposes:

  • Performance metrics and bench-marking inform business leaders of progress towards business goals (business process management).
  • Analytics quantify processes for a business to arrive at optimal decisions, and to perform business knowledge discovery. Analytics may variously involve data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, data lineage, complex event processing, and prescriptive analytics.
  • Business reporting can use BI data to inform strategy. Business reporting may involve data visualization, executive information system, and/or OLAP
  • BI can facilitate collaboration both inside and outside the business by enabling data sharing and electronic data interchange
  • Knowledge management is concerned with the creation, distribution, use, and management of business intelligence, and of business knowledge in general. Knowledge management leads to learning management and regulatory compliance.

The above is a brief about Business Intelligence. Watch this space for more updates on the latest trends in Technology.

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